Tool Group KPI Control for Reducing Semiconductor WIP
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Solution Overview
Problem
Traditional methodologies are inadequate for identifying key factors to reduce cycle time in complex manufacturing processes like semiconductor manufacturing, where dozens of stages with multiple steps are involved, leading to unreliable results.
Innovation Solution
A system and method that groups tools into tool groups, collects process profile data, calculates key performance indicators (KPIs) such as standard deviation of output from bottleneck tool groups, and uses a neural network model to analyze and control KPIs to reduce work-in-progress (WIP) and improve cycle time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methodologies (linear programming, computer simulation, queue model) are used to reduce cycle time, then the approach is simple and traditional, but the reliability is insufficient for complex manufacturing processes
Solution Approach 1:
The patent replaces traditional mechanical/mathematical methodologies (linear programming, computer simulation, queue models) with a data-driven machine learning approach. The system collects process profile data from multiple tool groups and uses machine learning algorithms to identify key performance indicators and their impacts on cycle time, providing more reliable insights for complex semiconductor manufacturing processes where traditional methods fail.
2Reliability
If process profile data is collected and machine learning is applied to identify key KPIs, then the reliability of cycle time reduction improves, but the system complexity increases
Solution Approach 1:
The patent segments the complex manufacturing system into multiple tool groups, each with specific process profile data. By dividing the overall system into manageable segments (tool groups performing different process steps), the system can collect and analyze data from each segment independently, then aggregate the insights to identify key performance indicators that impact overall cycle time, making the complex data collection process manageable and systematic.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between raw process profile data and actionable insights. The machine learning system acts as a mediator that automatically processes large volumes of process data, identifies patterns and key performance indicators, and translates them into actionable recommendations for cycle time reduction, eliminating the need for manual analysis of complex multi-stage manufacturing data.
3Productivity
If work-in-progress is reduced by controlling key KPIs, then the cycle time is improved, but the process control complexity increases
Solution Approach 1:
The patent implements a feedback-based control system that continuously monitors key performance indicators identified by machine learning algorithms. The system measures actual process outcomes, compares them against targets, and automatically adjusts process parameters to maintain optimal performance. This feedback mechanism enables dynamic control of work-in-progress levels and cycle time without requiring complex manual intervention, as the system self-regulates based on real-time data.
Data Source
AI summary
A method for improving a cycle time of a process of a product is provided. The method includes: collecting process profile data from a plurality of tool groups running the process, and calculating values of a plurality of key-performance-indicators (KPIs) of each tool group including calculating a standard deviation of an output of a stage of a bottleneck tool group of the tool groups; feeding the values of the KPIs and a work-in-progress (WIP) of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model; selecting a set of major KPIs of each tool group from the KPIs according to the impact of each tool group; and controlling the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP.